
The optimization problem is now on an Euclidean space, hence is it easy to use Euclidean algorithms like quasi-Newton methods (for instance, L-BFGS), or in the context of deep learning, momentum methods, Adam [1]}, or RMSProp.

It is also easy to implement: in a deep learning framework, the layer is parametrized with a simple skew-symmetric matrix, and there is no need to be careful with the optimizer.

